This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.
By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.
Led by interns from multiple DOE programs, a newly expanded dataset allows researchers to use easy-to-obtain measurements to determine the elemental composition of a promising carbon storage mineral.
PNNL’s year in review includes highlights ranging from advancing soil science to understanding Earth systems, expanding electricity transmission, detecting fentanyl, and applying artificial intelligence to aid scientific discovery.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
New research investigating water-lean solvents for carbon dioxide capture identifies the unique chemistry possible with their use, may lead to new design principles that move beyond single carbon capture.
Department of Energy’s Advanced Research Projects Agency-Energy selects PNNL project to help accelerate the development of marine carbon dioxide removal technologies.
This PNNL project was the focus of Nune’s talk when he delivered the keynote for the Carbon Capture and Utilization track at the 2nd Annual Baker Hughes Energy Frontiers Summit.
